Abbreviations
A-I Apolipoprotein
AI Artificial intelligence
ALB Albumin
ATP Adenosine triphosphate CE Capillary electrophoresis DAG Diacylglycerols
DAVID Database for Annotation, Visualisation and Integrated Discovery DDA Data-dependant acquisition
DHB Dihydroxybenzoic acid DIA Data independent acquisition ESI Electrospray ionization FGA Fibrinogen alpha
FID Flame ionization detection
FT-ICR Fourier transform ion cyclotron resonance G6PD Glucose-6 phosphate dehydrogenase
GC Gas chromatography
GPS Global positioning system
HDL High density lipoprotein cholesterol
HILIC Hydrophilic interaction liquid chromatography ICR Ion cyclotron resonance
IoT Internet of things IT Information technology
LC Liquid chromatography
LIT Linear ion trap
LPC Lyso-phosphatidylcholines MAG Monoacylglycerols
MALDI Matrix assisted laser desorption ionization
ML Machine learning
MS Mass spectrometry
NMR Nuclear magnetic resonance NPC Nasopharyngeal carcinoma
PC Phosphatidylcholines
PCK Phosphoenolpyruvate carboxykinase PE Phosphatidylethanolamine
PPP Pentose phosphate pathway PS Phosphatidylserine
PTM Post-translational modification RFID Radio frequency identification
SELDI Surface enhanced laser desorption ionisation SPSS Statistical Package for Social Sciences SRM Selected reaction monitoring
T2DM Type II diabetes mellitus TAG Triacylglycerol
TOF Time of flight
UPLC Ultra-performance liquid chromatography
References
Adua E, Russell A, Roberts P, et al. (2017). Innovation Analysis on Postgenomic Biomarkers: Glycomics for Chronic Diseases. OMICS 21(4), 183-196.
Aebersold R and Mann M (2016). Mass-spectrometric exploration of proteome structure and function. Nature 537(7620), 347-355.
Ali SE, Farag MA, Holvoet P, et al. (2016). A Comparative Metabolomics Approach Reveals Early Biomarkers for Metabolic Response to Acute Myocardial Infarction. Sci Rep 6, 1-14.
Anderson DC, Li W, Payan DG and Noble WS. (2003). A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: support vector machine classification of peptide MS/MS spectra and SEQUEST scores. J Proteome Res 2(2), 137-146.
Aretz I, and Meierhofer D (2016). Advantages and pitfalls of mass spectrometry based metabolome profiling in systems biology. Int J Mol Sci 17(5), 1-14.
Armbrust M, Fox A, Griffith R et al. (2010). A view of cloud computing. Commun. ACM 53(4), 50-58.
interface for high throughput processing of HPLC-glycan data. J Proteome Res, 9(4), 2037-2041.
Bauer M, Ahrné E, Baron AP, et al. (2014). Evaluation of data-dependent and- independent mass spectrometric workflows for sensitive quantification of proteins and phosphorylation sites. J Proteome Res 13(12), 5973-5988.
Blanksby SJ and Mitchell TW (2010). Advances in mass spectrometry for lipidomics. Annu Rev Anal Chem 3, 433-465.
Bones J, Mittermayr S, O’Donoghue N, Guttman AS and Rudd PM (2010). Ultra performance liquid chromatographic profiling of serum N-glycans for fast and efficient identification of cancer associated alterations in glycosylation. Anal Chem 82(24), 10208-10215.
Brand, H. R., & Wong, C. M. (1986, April). Application of knowledge based systems technology to triple quadrupole mass spectrometry (TQMS). In Proceedings of the Fifth National Conference on Artificial Intelligence (pp. 812-819).
Buyya R, Yeo, CS, Venugopal S., Broberg J and Brandic I (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comp Syst 25(6), 599-616.
Carbonnelle E, Mesquita C, Bille E et al. (2011). MALDI-TOF mass spectrometry tools for bacterial identification in clinical microbiology laboratory. Clin Biochem 44(1), 104-109.
Carvalho AS, Cuco CM, Lavareda C et al. (2017). Bronchoalveolar Lavage Proteomics in Patients with Suspected Lung Cancer. Sci Rep, 1-13.
Castelfranchi C (2013). Alan Turing’s “Computing Machinery and Intelligence”. Topoi, 32(2), 293-299.
Ceroni A, Maass K, Geyer H Geyer, et al. (2008). GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans. J Proteome Res 7(4), 1650-1659.
Chen X, Chen H, Dai M et al. (2016). Plasma lipidomics profiling identified lipid biomarkers in distinguishing early-stage breast cancer from benign lesions. Oncotarget, 7(24), 36622-36631.
Cho JY, Lee HJ, Jeong SK et al. (2015). Combination of multiple spectral libraries improves the current search methods used to identify missing proteins in the chromosome-centric human proteome project. J Proteome Res 14(12), 4959-
4966.
Chun J, Atalan E, Ward AC and Goodfellow M (1993). Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains. FEMS Microbiol Lett. 107(2-3), 321-326.
Chun J, Atalan E, Kim SB, et al. (1993). Rapid identification of streptomycetes by artificial neural network analysis of pyrolysis mass spectra. FEMS Microbiol Lett. 114(1), 115-119.
Cifani P, Shakiba M, Chhangawala S and Kentsis A (2017). ProteoModlR for functional proteomic analysis. BMC Bioinformatics, 18(1), 1-7.
Clancy T and Hovig E (2014). From proteomes to complexomes in the era of systems biology. Proteomics 14(1), 24-41.
Cohen PR and Feigenbaum EA (2014). The handbook of artificial intelligence (Vol. 3): Butterworth-Heinemann.
Combi C (2017). Editorial from the new Editor-in-Chief: Artificial Intelligence in Medicine and the forthcoming challenges. Artif Intell Med, 76, 37.
Comisarow, MB and Marshall AG (1974). Fourier transform ion cyclotron resonance spectroscopy. Chemical Physics Letters 25(2), 282-283.
Cooks RG, Ouyang Z Takats Z and Wiseman JM (2006). Ambient mass spectrometry. Science 311(5767), 1566-1570.
Costa C, Maraschin M and Rocha M (2015). An Integrated Computational Platform for Metabolomics Data Analysis. Paper presented at the 9th International Conference on Practical Applications of Computational Biology and Bioinformatics. 2015 (pp. 37-47). Springer, Cham.
Danikiewicz W (2013). Keith R. Jennings (Ed.): A history of European mass spectrometry. Anal Bioanal Chem 405(10), 3011-3012.
De Hoffmann E and Stroobant V (2007). Mass spectrometry: principles and
applications: John Wiley & Sons.
Dikaiakos MD, Katsaros D, Mehra P, et al. (2009). Cloud computing: Distributed internet computing for IT and scientific research. IEEE Internet computing, 13(5). Doerr A (2017). Global metabolomics. Nature Methods 14(1), 32-32.
Downard KM (2007). Francis William Aston: the man behind the mass spectrograph. Eur J Mass Spectrom 13(3), 177-190.
Data Analysis and Analytical Method Assessment. Anal Chem 88(6), 3156-3163. Dunn WB, Broadhurst D, Begley P et al. (2011). Procedures for large-scale metabolic
profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols 6(7), 1060- 1083.
Dunn WB, Broadhurst DI, Atherton HJ, et al. (2011). Systems level studies of mammalian metabolomes: the roles of mass spectrometry and nuclear magnetic resonance spectroscopy. Chem Soc Rev 40(1), 387-426.
Edmands WM, Ferrari P and Scalbert A (2014). Normalization to specific gravity prior to analysis improves information recovery from high resolution mass spectrometry metabolomic profiles of human urine. Anal Chem 86(21), 10925- 10931.
Ejsing CS, Duchoslav E, Sampaio J, et al. (2006). Automated identification and quantification of glycerophospholipid molecular species by multiple precursor ion scanning. Anal Chem 78(17), 6202-6214.
Elias JE, Haas W, Faherty BK, et al. (2005). Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations. Nature Methods 2(9), 667-675.
Fiedler K and Simons K (1995). The role of N-glycans in the secretory pathway. Cell, 81(3), 309-312.
French D, Sujishi KK, Long-Boyle JR, et al. (2014). Development and Validation of a Liquid Chromatography–Tandem Mass Spectrometry Assay to Quantify Plasma Busulfan. Ther Drug Monit 36(2), 169-174.
Fridriksson EK, Baird B and McLafferty FW (1999). Electrospray mass spectra from protein electroeluted from sodium dodecylsulfate polyacrylamide gel electrophoresis gels. J Am Soc Mass Spectrom 10(5), 453-455.
Gao Y, Wang X, Sang Z et al. (2017). Quantitative proteomics by SWATH-MS reveals sophisticated metabolic reprogramming in hepatocellular carcinoma tissues. Sci Rep 7, 1-12.
Gault, VA, and McClenaghan NH (2013). Understanding bioanalytical chemistry:
principles and applications: John Wiley & Sons.
Ge S, Wang Y, Song M, et al. (2018). Type 2 Diabetes Mellitus: Integrative Analysis of Multiomics Data for Biomarker Discovery. OMICS 22(7), 514-523.
German JB, Gillies LA, Smilowitz JT, et al. (2007). Lipidomics and lipid profiling in metabolomics. Curr Opin Lipidol 18(1), 66-71.
Ghaste M, Mistrik R and Shulaev V (2016). Applications of Fourier transform ion cyclotron resonance (FT-ICR) and orbitrap based high resolution mass spectrometry in metabolomics and lipidomics. Int J Mol Sci 17(6), 1-22.
Gika HG, Wilson, ID and Theodoridis GA (2014). LC–MS-based holistic metabolic profiling. Problems, limitations, advantages, and future perspectives. J Chromatogr B 966, 1-6.
Gizaw ST, Ohashi T, Tanaka M, et al. (2016). Glycoblotting method allows for rapid and efficient glycome profiling of human Alzheimer's disease brain, serum and cerebrospinal fluid towards potential biomarker discovery. Biochim Biophys Acta Gen Subj 1860(8), 1716-1727.
Grapov D, Fahrmann J, Wanichthanarak K and Khoomrung S (2018). Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine. OMICS 1, 1-7.
Griffiths IW (1997). JJ. Thomson— the Centenary of His Discovery of the Electron and of His Invention of Mass Spectrometry. Rapid Commun Mass Spectrom 11(1), 2-16.
Hamet P and Tremblay J (2017). Artificial intelligence in medicine. Metabolism 69, S36- S40.
González-Peña D, Dudzik D, Colina-Coca C, et al. (2016). Multiplatform metabolomic fingerprinting as a tool for understanding hypercholesterolemia in Wistar rats. Eur J Nutr 55(3), 997-1010.
Hamm G, Bonnel D, Legouffe R, et al. (2012). Quantitative mass spectrometry imaging of propranolol and olanzapine using tissue extinction calculation as normalization factor. J Proteomics 75(16), 4952-4961.
Hardman M and Makarov AA. (2003). Interfacing the orbitrap mass analyzer to an electrospray ion source. Anal Chem 75(7), 1699-1705.
Hargittai I. (2007). Gerald E. Brown and Chang-Hwan Lee, Hans Bethe and His Physics. Struct Chem 18(5), 723-724.
Hartler J, Trötzmüller M, Chitraju C, et al. (2011). Lipid Data Analyzer: unattended identification and quantitation of lipids in LC-MS data. Bioinformatics, 27(4), 572-577.
Hasin Y, Seldin M and Lusis A (2017). Multi-omics approaches to disease. Genome Biology, 18(1), 83.
Hebert DN and Molinari M (2007). In and out of the ER: protein folding, quality control, degradation, and related human diseases. Physiol Rev 87(4), 1377-1408. Helenius A and Aebi M (2001). Intracellular functions of N-linked glycans. Science
291(5512), 2364-2369.
Helenius A and Aebi M (2004). Roles of N-linked glycans in the endoplasmic reticulum. Ann Rev Biochem 73(1), 1019-1049.
Hevesy, G (1948). Francis William Aston. 1877-1945. Obituary Notices of Fellows of the
Royal Society 5(16), 635-650.
Hizal DB, Wolozny D Colao J, et al. (2014). Glycoproteomic and glycomic databases. Clin Proteomics 11(1), 15.
Hoffmann ED and Stroobant V (2001). Mass spectrometry: principles and applications: England: Wiley.
Hoopmann MR and Moritz RL (2013). Current algorithmic solutions for peptide-based proteomics data generation and identification. Curr Opinion Biotechnol 24(1), 31-38.
Howells SL, Maxwell RJ, Peet AC, et al. (1992). An Investigation of Tumor 1H Nuclear
Magnetic Resonance Spectra by the Application of Chemometric Techniques. Magnetic Resonance in Medicine. 28(2), 214-236.
Hsieh KT, Liu PH and Urban PL (2015). Automated on-line liquid–liquid extraction system for temporal mass spectrometric analysis of dynamic samples. Analytica Chimica Acta 894, 35-43.
Hu C, van der Heijden R, Wang M, et al. (2009). Analytical strategies in lipidomics and applications in disease biomarker discovery. J Chromatogr B 877(26), 2836- 2846.
Hu Q, Noll RJ, Li H, et al. (2005). The Orbitrap: a new mass spectrometer. J Mass Spectrom 40(4), 430-443.
Huang DW, Sherman BT, and Lempicki RA (2009). Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 37(1), 1-13.
Huffman JE, Pučić-Baković M, Klarić L, et al., (2014). Comparative performance of four methods for high-throughput glycosylation analysis of immunoglobulin G in
genetic and epidemiological research. Mol Cell Proteomics 13(6), 1598-1610. Hurst GB, Weaver K, Doktycz MJ, et al. (1998). MALDI-TOF analysis of polymerase chain
reaction products from methanotrophic bacteria. Anal Chem 70(13), 2693- 2698.
Hutchens TW and Yip TT. (1993). New desorption strategies for the mass spectrometric analysis of macromolecules. Rapid Commun Mass Spectrom 7(7), 576-580. Igl W, Polašek O, Gornik O et al. (2011). Glycomics meets lipidomics—associations of
N-glycans with classical lipids, glycerophospholipids, and sphingolipids in three European populations. Mol BioSyst 7(6), 1852-1862.
Itoh N, Sakaue S, Nakagawa H et al. (2007). Analysis of N-glycan in serum glycoproteins from db/db mice and humans with type 2 diabetes. Am J Physiol Endocrinol Metab 293(4), E1069-E1077.
Jansen BC, Bondt A, Reiding KR, et al. (2016). MALDI-TOF-MS reveals differential N- linked plasma-and IgG-glycosylation profiles between mothers and their newborns. Sci Rep 6, 1-11.
Jansen BC, Reiding KR, Bondt A, et al. (2015). MassyTools: a high-throughput targeted data processing tool for relative quantitation and quality control developed for glycomic and glycoproteomic MALDI-MS. J Proteome Res 14(12), 5088-5098. Jin X, Su T, Kong J, et al. (2018.) State-of-the-Art mobile intelligence: enabling robots
to move like humans by Estimating Mobility with Artificial Intelligence. Applied Sci 8(3), 379.
Karas M, Bahr U and Gießmann U (1991). Matrix‐assisted laser desorption ionization mass spectrometry. Mass Spectrom Rev 10(5), 335-357.
Karas M and Hillenkamp F (1988). Laser desorption ionization of proteins with molecular masses exceeding 10,000 daltons. Anal Chem 60(20), 2299-2301. Kauppila TJ, Talaty N, Salo PK, et al. (2006). New surfaces for desorption electrospray
ionization mass spectrometry: porous silicon and ultra‐thin layer chromatography plates. Rapid Commun Mass Spectrom 20(14), 2143-2150. Kessler N, Neuweger H, Bonte A, et al. (2013). MeltDB 2.0–advances of the
metabolomics software system. Bioinformatics 29(19), 2452-2459.
Kim W, Bennett EJ, Huttlin et al. (2011). Systematic and quantitative assessment of the ubiquitin-modified proteome. Mol Cell 44(2), 325-340.
Biochim Biophys Acta Gen Subj 1861(10),2447-54.
Kondrat RW and Cooks RG (1978). Direct analysis of mixtures by mass spectrometry. Anal. Chem. 50, 81–92A.
Konijnenberg A, Butterer A and Sobott F (2013). Native ion mobility-mass spectrometry and related methods in structural biology. Biochim Biophys Proteins Proteomics 1834(6), 1239-1256.
Kontush A and Chapman MJ (2010). Lipidomics as a tool for the study of lipoprotein metabolism. Curr Atheroscler Rep 12(3), 194-201.
Kopetz H (2011). Real-time systems: design principles for distributed embedded applications: Springer Science & Business Media.
Larance M and Lamond AI (2015). Multidimensional proteomics for cell biology. Nat Rev Mol Cell Biol 16(5), 269-280.
Lau KS and Dennis JW (2008). N-Glycans in cancer progression. Glycobiology 18(10), 750-760.
Lauc G, Essafi A, Huffman JE, Hayward C et al. (2010). Genomics meets glycomics-the first GWAS study of human N-Glycome identifies HNF1-α as a master regulator of plasma protein fucosylation. PLoS Genet 6(12), e1001256.
Lehmann WD (2016). A timeline of stable isotopes and mass spectrometry in the life sciences. Mass Spectrom. Rev. 36(1), 58-85.
Lenk A, Klems M, Nimis J, et al. (2009). What's inside the Cloud? An architectural map
of the Cloud landscape. Paper presented at the Proceedings of the 2009 ICSE
Workshop on Software Engineering Challenges of Cloud Computing.
Li B, Tang J, Yang Q et al. (2016). Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis. Sci Rep 6, 1-13.
Li J, Ren S, Piao Hl, et al. (2016). Integration of lipidomics and transcriptomics unravels aberrant lipid metabolism and defines cholesteryl oleate as potential biomarker of prostate cancer. Sci Rep 6, 1-11.
Liao HW, Tsai IL, Chen GY, et al. (2014). Quantification of target analytes in various biofluids using a postcolumn infused-internal standard method combined with matrix normalization factors in liquid chromatography–electrospray ionization mass spectrometry. J Chromatogr A, 1358, 85-92.
in blood lipids and dyslipidaemia. J Transl Med 16(1), 1-10.
Liu D, Zhao Z, Wang A, et al. (2018). Ischemic stroke is associated with the pro- inflammatory potential of N-glycosylated immunoglobulin G Neuroflammation, 15(1), 123.
Liu X, Inbar Y, Dorrestein PC et al. (2010). Deconvolution and database search of complex tandem mass spectra of intact proteins a combinatorial approach. Mol Cell Proteomics 9(12), 2772-2782.
Llop E, Ferrer-Batallé M, Barrabés S, et al. (2016). Improvement of Prostate Cancer Diagnosis by Detecting PSA Glycosylation-Specific Changes. Theranostics 6(8):1190-204.
Lu JP, Knezevic A, Wang YX, et al. (2011). Screening novel biomarkers for metabolic syndrome by profiling human plasma N-glycans in Chinese Han and Croatian populations. J Proteome Research 10(11), 4959-4969.
Lu J, Huang Y, Wang Y, et al. (2012). Profiling plasma peptides for the identification of potential ageing biomarkers in Chinese Han adults. PloS One, 7(7), e39726. Luo Y, Mok TS, Lin X, et al. (2017). SWATH-based proteomics identified carbonic
anhydrase 2 as a potential diagnosis biomarker for nasopharyngeal carcinoma. Sci Rep 7. 1-11.
Lütteke T, Bohne-Lang A, Loss A, et al. (2006). GLYCOSCIENCES. de: an Internet portal to support glycomics and glycobiology research. Glycobiology, 16(5), 71R-81R. Makarov A (2000). Electrostatic axially harmonic orbital trapping: a high-performance
technique of mass analysis. Anal Chem 72(6), 1156-1162.
Makarov A, Denisov E, Kholomeev A, et al. (2006). Performance evaluation of a hybrid linear ion trap/orbitrap mass spectrometer. Anal Chem 78(7), 2113-2120. Mallick P and Kuster B (2010). Proteomics: a pragmatic perspective. Nature Biotechnol
28(7), 695-709.
Mathew AK and Padmanaban V (2013). Metabolomics: the apogee of the omics trilogy. Int J Pharm Pharm Sci 5(2), 45-48.
McCudden C (2017). The future of artificial intelligence and interpretative specialization in clinical biochemistry. Clin Biochem 50(6), 253-254.
McLachlan F, Timofeeva M, Bermingham M, et al. (2016). A Case-control Study in an Orcadian Population Investigating the Relationship between Human Plasma N- glycans and Metabolic Syndrome. J Glycomics Lipidomics 6(139), 2153-0637.
Meng Q, Ge S, Yan W, et al. (2016). Screening for potential serum‐based proteomic biomarkers for human type 2 diabetes mellitus using MALDI‐TOF MS. Proteomics Clin Appl 11(3-4), 1600079.
Mereiter S, Magalhães A, Adamczyk B et al. (2016). Glycomic analysis of gastric carcinoma cells discloses glycans as modulators of RON receptor tyrosine kinase activation in cancer. Biochim et Biophys Acta Gen Subj 1860(8), 1795- 1808.
Mohammed Y, Mostovenko E, Henneman AA, et al. (2012). Cloud parallel processing of tandem mass spectrometry based proteomics data. J Proteomics Res 11(10), 5101-5108.
Muddiman DC and Oberg AL (2005). Statistical evaluation of internal and external mass calibration laws utilized in Fourier transform ion cyclotron resonance mass spectrometry. Anal Chem 77(8), 2406-2414.
Nilsson NJ (2014). Principles of artificial intelligence: Morgan Kaufmann.
Özdemir V and Hekim N (2018). Birth of industry 5.0: Making sense of big data with artificial intelligence,“the internet of things” and next-generation technology policy. OMICS 22(1), 65-76.
PARODI AJ (2000). Role of N-oligosaccharide endoplasmic reticulum processing reactions in glycoprotein folding and degradation. Biochem J 348(1), 1-13. Patel VL, Shortliffe EH, Stefanelli M, et al. (2009). The coming of age of artificial
intelligence in medicine. Artif Intell Med 46(1), 5-17.
Patterson SD and Aebersold RH (2003). Proteomics: the first decade and beyond. Nature Genet 33, 311-323.
Patti GJ, Yanes O, and Siuzdak G (2012). Innovation: Metabolomics: the apogee of the omics trilogy. Nature Rev Mol Cell Biol 13(4), 263-269.
Paul W (1990). Electromagnetic traps for charged and neutral particles. Rev Mod Phys 62(3), 531.
Place J F, Truchaud A, Ozawa K, et al. (1994). Use of artificial intelligence in analytical systems for the clinical laboratory. Clinica chimica acta, 231(2), S1-S34.
Pluskal T, Castillo S, Villar-Briones A, et al. (2010). MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC bioinformatics, 11(1), 395.
practical advices. Expert Rev Proteomics 4(1), 51-65.
Porter CJ and Bereman MS (2015). Data-independent-acquisition mass spectrometry for identification of targeted-peptide site-specific modifications. Anal Bioanal Chem 407(22), 6627-6635.
Psychogios N, Hau DD, Peng J et al. (2011). The human serum metabolome. 6(2), e16957.
Rai S, and Bhatnagar S (2017). Novel Lipidomic Biomarkers in Hyperlipidemia and Cardiovascular Diseases: An Integrative Biology Analysis. OMICS 21(3), 132-142. Ramakrishnan P, Nair S, and Rangiah K (2016). A method for comparative metabolomics in urine using high resolution mass spectrometry. J Chromatogr A, 1443, 83-92.
Ranzinger R, Herget S, von der Lieth CW, et al. (2011). GlycomeDB—a unified database for carbohydrate structures. Nucleic Acids Res 39(suppl 1), D373-D376. Russell A, Adua E, Ugrina I, et al. (2018). Unravelling Immunoglobulin G Fc N-
Glycosylation: A Dynamic Marker Potentiating Predictive, Preventive and Personalised Medicine. Int J Mol Sci 19(2) 1-18.
Russell AC, ŠM, Garcia MT, Novokmet M, et al. (2017). The N-glycosylation of immunoglobulin G as a novel biomarker of Parkinson's disease. Glycobiology, 27(5), 501-510.
Russell H and Siuzdak G (2003). A Mass Spec Timeline. Today’S Chemist AT Work. Russell S and Norvig P (2010). Artificial intelligence: a modern approach. 3rd ed. Upper
Saddle River, NJ: Prentice Hall.
Salih B, Masselon C and Zenobi R (1998). Matrix‐assisted laser desorption/ionization mass spectrometry of noncovalent protein–transition metal ion complexes. J Mass Spectrom 33(10), 994-1002.
Sandra K and Sandra P (2013). Lipidomics from an analytical perspective. Curr Opin Chem Biol 17(5), 847-853.
Satten GA, Datta S, Moura H, et al. (2004). Standardization and denoising algorithms for mass spectra to classify whole-organism bacterial specimens. 20(17), 3128- 3136.
Savolainen OI, Sandberg AS, and Ross AB (2015). A simultaneous metabolic profiling and quantitative multimetabolite metabolomic method for human plasma using gas-chromatography tandem mass spectrometry. J Proteome Res 15(1),
259-265.
Schadt EE, Linderman MD, Sorenson J, et al. (2010). Computational solutions to large- scale data management and analysis. Nature Rev Genet 11(9), 647-657. Schilling O, auf dem Keller U and Overall CM (2011). Protease specificity profiling by
tandem mass spectrometry using proteome-derived peptide libraries. Gel-Free Proteomics: Methods and Protocols 257-272.
Schwudke D, Oegema J, Burton L et al. (2006). Lipid profiling by multiple precursor and neutral loss scanning driven by the data-dependent acquisition. Anal Chem 78(2), 585-595.
Schwudke D, Schuhmann K, Herzog R, et al. (2011). Shotgun lipidomics on high resolution mass spectrometers. Cold Spring Harb Perspect Biol 3(9), a004614. Scigelova M and Makarov, A (2006). Orbitrap mass analyzer–overview and applications
in proteomics. Proteomics 6(S2), 16-21.
Scott D R (1991). Expert system for estimates of molecular weights of volatile organic compounds from low-resolution mass spectra. Analytica Chimica Acta 246(2), 391-403.
Sebastian A, Alzain MA, Asweto CO, et al. (2016). Glycan biomarkers for rheumatoid arthritis and its remission status in Han Chinese patients. Omics: a journal of integrative biology, 20(6), 343-351.
Seng P, Rolain JM, Fournier PE, et al. (2010). MALDI-TOF-mass spectrometry applications in clinical microbiology. Future Microbiol 5(11), 1733-1754. Serna J, García-Seisdedos D, Alcázar A, et al. (2015). Quantitative lipidomic analysis of
plasma and plasma lipoproteins using MALDI-TOF mass spectrometry. Chem Phys Lipids 189, 7-18.
Sethi S and Brietzke E (2016). Recent Advances in Lipidomics: Analytical and Clinical Perspectives. Prostaglandins Other Lipid Mediat. 128, 8-16.
Shevchenko A and Simons K (2010). Lipidomics: coming to grips with lipid diversity. Nature Rev Mol Cell biol 11(8), 593-598.
Sheynkman GM, Shortreed MR, Cesnik AJ, et al. (2016). Proteogenomics: Integrating Next-Generation Sequencing and Mass Spectrometry to Characterize Human Proteomic Variation. Annu Rev Anal Chem 9, 521-545.
Shi T, Song E, Nie S, et al. (2016). Advances in targeted proteomics and applications to biomedical research. Proteomics 16(15-16), 2160-2182.
Smith RD, Loo JA, Loo RRO, et al. (1991). Principles and practice of electrospray ionization? mass spectrometry for large polypeptides and proteins. Mass Spectrom Rev 10(5), 359-452.